Abstract/Project Summary The cerebellum is known to be a key part of the brain's sensory-motor processing. The basic features of cerebellar learning mechanisms are well characterized. This learning is the service of improving feed- forward predictions, which for much of the cerebellum contribute to the accuracy of movements. This proposal is about how the cerebellum copes with noisy inputs and is based on extensive preliminary data demonstrating a novel computation implemented by the cerebellum that makes its responses to noisy inputs more adaptive. We begin by training the cerebellum to output a response of a target size to a particular pattern of its mossy fiber inputs. By systematically varying the dissimilarity of probe mossy fiber inputs we see that the cerebellum does not decrease response amplitude as the dissimilarity increases. Doing so would be non-adaptive, as there is no prior experience that these smaller amplitudes are correct. Instead, the cerebellum decreases the likelihood of making a response as the dissimilarity increases, but when it does respond it almost always produces the correct amplitude. We present abundant preliminary data that this adaptive behavior is not attributable to the response system being inherently all-or-none. The same data demonstrate that this computation is cerebellar and is, indeed, largely accomplished in the cerebellar cortex. The responses of Purkinje cells, the sole output of the cerebellar cortex, are also all-or-none and track the behavior on a trial-by- trial basis. In three specific aims, we propose to identify the mechanisms of this adaptive computation. We will first complete recording studies that identify over a wide range of conditions the relative contributions of processing in the cerebellar cortex versus processing in the cerebellar deep nuclei. Second, we will use recordings from cerebellar cortex neurons to determine the mechanisms that make responding to uncertain inputs stochastic. In particular, we will determine whether there are significant differences in inputs to the Purkinje cells on response versus non-response trials and we will determine whether newly discovered connectivity between Purkinje cells and basket cells play a role in amplifying or thresholding these differences to make responding stochastic. Finally, we will use a combination of reversible inactivation techniques and recordings of Purkinje cells to test the hypothesis that newly discovered collaterals of deep cerebellar nucleus neurons implement a form of efference copy feedback that enforce the proper response amplitude even for inputs that are so different that they rarely elicit a response. Completion of these studies will identify the mechanisms of a novel computational adaptation of the cerebellum related to coping with noisy or uncertain inputs. Such mechanisms may be an under-appreciated category of etiology of neural pathologies, owing largely to the fact that most experimental approaches seek to eliminate as much noise or variability as possible. As such, these studies represent a novel and innovative approach to the mechanisms of brain function and dysfunction.